context("Integration Tests - Full Workflows")

test_that("complete workflow without DK works", {
  # Create test data - realistic learning scenario
  set.seed(123)  # For reproducible results
  n_obs <- 20
  n_items <- 3
  
  # Generate pre-test data (some knowledge)
  pre_test <- data.frame(
    item1 = sample(c(0, 1), n_obs, replace = TRUE, prob = c(0.7, 0.3)),
    item2 = sample(c(0, 1), n_obs, replace = TRUE, prob = c(0.6, 0.4)),
    item3 = sample(c(0, 1), n_obs, replace = TRUE, prob = c(0.8, 0.2))
  )
  
  # Generate post-test data (improved knowledge)
  pst_test <- data.frame(
    item1 = pmax(pre_test$item1, sample(c(0, 1), n_obs, replace = TRUE, prob = c(0.4, 0.6))),
    item2 = pmax(pre_test$item2, sample(c(0, 1), n_obs, replace = TRUE, prob = c(0.3, 0.7))),
    item3 = pmax(pre_test$item3, sample(c(0, 1), n_obs, replace = TRUE, prob = c(0.5, 0.5)))
  )
  
  # Step 1: Create transition matrix
  trans_matrix <- multi_transmat(pre_test, pst_test)
  
  expect_true(is.matrix(trans_matrix))
  expect_equal(nrow(trans_matrix), n_items)
  expect_equal(ncol(trans_matrix), 4)  # No DK, so 4 columns
  expect_true(all(trans_matrix >= 0))
  expect_equal(sum(trans_matrix), n_obs * n_items)
  
  # Step 2: Fit LCA model
  lca_results <- lca_cor(trans_matrix)
  
  expect_true(is.list(lca_results))
  expect_true("param.lca" %in% names(lca_results))
  expect_true("est.learning" %in% names(lca_results))
  expect_equal(ncol(lca_results$param.lca), n_items)
  expect_equal(nrow(lca_results$param.lca), 4)  # lgg, lgk, lkk, gamma
  
  # Parameters should be reasonable
  expect_true(all(lca_results$param.lca >= 0, na.rm = TRUE))
  expect_true(all(lca_results$param.lca <= 1, na.rm = TRUE))
  
  # Step 3: Calculate goodness of fit
  fit_stats <- fit_model(pre_test, pst_test, 
                         lca_results$param.lca["gamma", ], 
                         lca_results$param.lca[1:3, ])
  
  expect_true(is.matrix(fit_stats))
  expect_equal(dim(fit_stats), c(2, n_items))
  expect_equal(rownames(fit_stats), c("chi-square", "p-value"))
  expect_true(all(fit_stats["chi-square", ] >= 0))
  expect_true(all(fit_stats["p-value", ] >= 0 & fit_stats["p-value", ] <= 1))
  
  # Step 4: Standard correction for comparison
  lucky <- rep(0.25, n_items)  # Assume 4-option multiple choice
  std_results <- stnd_cor(pre_test, pst_test, lucky)
  
  expect_true(is.list(std_results))
  expect_true(all(c("pre", "pst", "learn") %in% names(std_results)))
  expect_equal(length(std_results$learn), n_items)
})

test_that("complete workflow with DK works", {
  # Create test data with don't know responses
  n_obs <- 15
  
  # Generate mixed responses including DK
  pre_test <- data.frame(
    item1 = sample(c("0", "1", "d"), n_obs, replace = TRUE, prob = c(0.4, 0.3, 0.3)),
    item2 = sample(c("0", "1", "d"), n_obs, replace = TRUE, prob = c(0.5, 0.2, 0.3)),
    stringsAsFactors = FALSE
  )
  
  pst_test <- data.frame(
    item1 = sample(c("0", "1", "d"), n_obs, replace = TRUE, prob = c(0.3, 0.4, 0.3)),
    item2 = sample(c("0", "1", "d"), n_obs, replace = TRUE, prob = c(0.2, 0.5, 0.3)),
    stringsAsFactors = FALSE
  )
  
  # Full workflow
  trans_matrix <- multi_transmat(pre_test, pst_test)
  
  expect_true(is.matrix(trans_matrix))
  expect_equal(nrow(trans_matrix), 2)
  expect_equal(ncol(trans_matrix), 9)  # DK present, so 9 columns
  
  # LCA with DK data
  lca_results <- lca_cor(trans_matrix)
  
  expect_equal(nrow(lca_results$param.lca), 8)  # DK model has 8 parameters
  expect_equal(ncol(lca_results$param.lca), 2)
  
  # All parameters should be valid
  expect_true(all(lca_results$param.lca >= 0, na.rm = TRUE))
  expect_true(all(lca_results$param.lca <= 1, na.rm = TRUE))
})

test_that("workflow handles edge cases gracefully", {
  # Case 1: No learning (identical pre and post)
  pre_identical <- data.frame(
    item1 = c(1, 0, 1, 0, 1),
    item2 = c(0, 1, 0, 1, 0)
  )
  pst_identical <- pre_identical  # Identical
  
  trans_matrix <- multi_transmat(pre_identical, pst_identical)
  
  # Should have only diagonal transitions (x00 and x11)
  expect_equal(as.numeric(trans_matrix[, "x01"]), rep(0, 2))
  expect_equal(as.numeric(trans_matrix[, "x10"]), rep(0, 2))
  
  # LCA should still work
  lca_results <- lca_cor(trans_matrix)
  expect_true(all(lca_results$est.learning <= 0.1, na.rm = TRUE))  # Little to no learning
  
  # Case 2: Perfect learning (all wrong -> all right)
  pre_wrong <- data.frame(item1 = rep(0, 10))
  pst_right <- data.frame(item1 = rep(1, 10))
  
  trans_matrix_perfect <- multi_transmat(pre_wrong, pst_right)
  
  # Should have only x01 transitions
  expect_equal(trans_matrix_perfect[, "x00"], 0)
  expect_equal(trans_matrix_perfect[, "x10"], 0)
  expect_equal(trans_matrix_perfect[, "x11"], 0)
  expect_equal(trans_matrix_perfect[, "x01"], 10)
  
  # Case 3: Mixed NA values
  pre_na <- data.frame(
    item1 = c(1, NA, 0, 1, NA),
    item2 = c(NA, 1, NA, 0, 1)
  )
  pst_na <- data.frame(
    item1 = c(0, 1, NA, 1, 1),
    item2 = c(1, NA, 1, NA, 0)
  )
  
  # Should handle NAs gracefully (converted to 0)
  trans_matrix_na <- multi_transmat(pre_na, pst_na)
  expect_true(is.matrix(trans_matrix_na))
  expect_equal(sum(trans_matrix_na), 10)  # 5 observations * 2 items
})

test_that("workflows are consistent between approaches", {
  # Test that different methods give consistent results for same data
  set.seed(456)
  
  pre_test <- data.frame(
    item1 = sample(c(0, 1), 20, replace = TRUE),
    item2 = sample(c(0, 1), 20, replace = TRUE)
  )
  pst_test <- data.frame(
    item1 = sample(c(0, 1), 20, replace = TRUE),
    item2 = sample(c(0, 1), 20, replace = TRUE)
  )
  
  # Method 1: Step by step
  trans1 <- multi_transmat(pre_test, pst_test)
  lca1 <- lca_cor(trans1)
  
  # Method 2: Direct approach with same data
  trans2 <- multi_transmat(pre_test, pst_test)
  lca2 <- lca_cor(trans2)
  
  # Results should be identical
  expect_equal(trans1, trans2)
  expect_equal(lca1$param.lca, lca2$param.lca)
  expect_equal(lca1$est.learning, lca2$est.learning)
  
  # Standard correction should be deterministic
  lucky <- c(0.25, 0.25)
  std1 <- stnd_cor(pre_test, pst_test, lucky)
  std2 <- stnd_cor(pre_test, pst_test, lucky)
  
  expect_equal(std1, std2)
})

test_that("subgroup analysis works in complete workflow", {
  # Create larger dataset
  n <- 30
  pre_test <- data.frame(
    item1 = sample(c(0, 1), n, replace = TRUE),
    item2 = sample(c(0, 1), n, replace = TRUE)
  )
  pst_test <- data.frame(
    item1 = sample(c(0, 1), n, replace = TRUE),
    item2 = sample(c(0, 1), n, replace = TRUE)
  )
  
  # Define subgroup (first half)
  subgroup <- rep(c(TRUE, FALSE), each = n/2)
  
  # Full sample analysis
  trans_full <- multi_transmat(pre_test, pst_test)
  lca_full <- lca_cor(trans_full)
  
  # Subgroup analysis
  trans_sub <- multi_transmat(pre_test, pst_test, subgroup = subgroup)
  lca_sub <- lca_cor(trans_sub)
  
  # Subgroup should have half the observations
  expect_equal(sum(trans_sub), sum(trans_full) / 2)
  
  # Both should produce valid results
  expect_true(all(lca_full$param.lca >= 0, na.rm = TRUE))
  expect_true(all(lca_sub$param.lca >= 0, na.rm = TRUE))
  expect_true(all(lca_full$param.lca <= 1, na.rm = TRUE))
  expect_true(all(lca_sub$param.lca <= 1, na.rm = TRUE))
  
  # Standard correction with subgroup
  std_full <- stnd_cor(pre_test, pst_test, c(0.25, 0.25))
  std_sub <- stnd_cor(pre_test[subgroup, ], pst_test[subgroup, ], c(0.25, 0.25))
  
  expect_equal(length(std_full$learn), 2)
  expect_equal(length(std_sub$learn), 2)
})